Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A vision system comprising: a robotic arm; a three-dimensional (3D) camera disposed on the robotic arm; a memory operable to store teat location information for a dairy livestock; and a processor operably coupled to the robotic arm, the 3D camera, and the memory, and configured to: position the robotic arm adjacent to the dairy livestock; acquire a 3D image using the 3D camera, wherein each pixel of the 3D image is associated with a depth value; identify a set of teat candidates within the 3D image, wherein each teat candidate is associated with a teat location and a set of pixels in the 3D image; filter the set of teat candidates, wherein filtering the set of teat candidates comprises: identifying a set of A-points for a teat candidate, wherein A-points are pixels within a first distance threshold of the teat location for the teat candidate; identifying a set of B-points for the teat candidate, wherein B-points are pixels within a second distance threshold of the teat location for the teat candidate, wherein the second distance threshold is greater than the first distance threshold; determining whether the teat candidate satisfies one or more filtering rules based on the A-points and the B-points; and removing the teat candidate from the set of teat candidates in response to determining that the teat candidate fans to satisfy at least one of the filtering rules; determine an aggregate teat candidate score for the set of teat candidates, wherein the aggregate teat candidate score is based at least in part on distances between teat candidates and the teat locations for the set of teat candidates of the dairy livestock; compare the aggregate teat candidate score to a score threshold value; and update the teat location information for the dairy livestock in response to determining that the aggregate teat candidate score is greater than or equal to the score threshold value.
2. The system of claim 1 , wherein determining whether the teat candidate satisfies one or more filtering rules comprises: determining a total number of A-points and B-points for the teat candidate; comparing the total number of A-points and B-points to a total point count value; and determining the teat candidate satisfies a filtering rule in response to determining that the total number of A-points and B-points is greater than or equal to the total point count value.
This invention relates to automated teat detection in livestock, particularly for milking systems. The problem addressed is accurately identifying teat locations on animals to ensure proper attachment of milking equipment, improving efficiency and reducing errors in automated milking systems. The system analyzes teat candidates by evaluating specific feature points, referred to as A-points and B-points, which are key indicators of teat presence. A-points and B-points are likely derived from image processing or sensor data, representing distinct characteristics of a teat's shape or position. The system counts these points for each teat candidate and compares the total against a predefined threshold value. If the combined count of A-points and B-points meets or exceeds this threshold, the candidate is confirmed as a valid teat. This filtering mechanism helps eliminate false positives, ensuring only genuine teats are selected for milking. The system may also include additional processing steps, such as image segmentation or contour analysis, to initially identify potential teat candidates before applying the point-based filtering. The threshold value can be adjusted based on animal species, lighting conditions, or other environmental factors to improve detection accuracy. This approach enhances the reliability of automated milking systems by reducing misattachments and improving operational efficiency.
3. The system of claim 1 , wherein determining whether the teat candidate satisfies one or more filtering rules comprises: determining a total number of A-points for the teat candidate; comparing the total number of A-points with an A-point count threshold value; and determining the teat candidate satisfies a filtering rule in response to determining that the total number of A-points is greater than or equal to the A-point count threshold value.
A system for identifying teat candidates in an image analyzes the image to detect potential teat locations. The system applies filtering rules to these candidates to distinguish true teats from false positives. One such filtering rule involves counting A-points, which are specific features or markers associated with the teat candidate. The system calculates the total number of A-points for each candidate and compares this count against a predefined threshold. If the count meets or exceeds the threshold, the candidate is deemed valid and retained for further processing. This rule helps eliminate false detections by ensuring only candidates with sufficient A-point evidence are considered. The system may use additional filtering rules to further refine the selection, but the A-point count threshold serves as a key criterion for initial validation. This approach improves accuracy in teat detection by leveraging feature-based analysis to filter out unreliable candidates early in the process.
4. The system of claim 1 , wherein determining whether the teat candidate satisfies one or more filtering rules comprises: determining a width of the teat candidate based at least in part on the A-points; comparing the width of the teat candidate to a width range limit; and determining the teat candidate satisfies a filtering rule in response to determining that the width of the teat candidate is within the width range limit.
This invention relates to automated systems for identifying and analyzing teat candidates in livestock, particularly for tasks such as milking or health monitoring. The system addresses the challenge of accurately detecting and validating teat candidates in images or sensor data to ensure proper equipment positioning and animal welfare. The system processes image or sensor data to identify potential teat candidates based on key anatomical points, referred to as A-points. These points are used to calculate the width of each teat candidate. The system then applies filtering rules by comparing the calculated width against predefined width range limits. If the width falls within the acceptable range, the teat candidate is deemed valid. This filtering step helps eliminate false positives, such as non-teat objects or anatomical anomalies, ensuring only legitimate teat candidates are selected for further processing. The system may also include additional validation steps, such as verifying the spatial relationship between multiple teat candidates or assessing symmetry, to further refine detection accuracy. By dynamically adjusting filtering parameters based on animal breed, age, or environmental conditions, the system improves adaptability and reliability in real-world farming applications. This approach enhances automation in dairy farming, reducing manual intervention and improving operational efficiency.
5. The system of claim 1 , wherein determining whether the teat candidate satisfies one or more filtering rules comprises: determining a height of the teat candidate based at least in part on the A-points; comparing the height of the teat candidate to a height range limit; and determining the teat candidate satisfies a filtering rule in response to determining that the height of the teat candidate is within the height range limit.
This invention relates to automated livestock monitoring systems, specifically for identifying and filtering teat candidates in images of animals, such as cows, to ensure accurate detection for milking or health monitoring. The problem addressed is the challenge of reliably distinguishing true teats from false positives in images, which can lead to errors in automated milking or health assessment systems. The system processes images to detect potential teat candidates by analyzing key points, such as A-points, which are reference points on the animal's body. The system then evaluates these candidates by determining their height based on the A-points. The height is compared against a predefined height range limit to filter out candidates that fall outside this range, ensuring only valid teats are selected. This filtering step improves the accuracy of teat detection by eliminating false positives that do not meet the expected height criteria. The system may also include additional filtering rules, such as shape or position analysis, to further refine the detection process. By combining multiple filtering criteria, the system enhances the reliability of teat identification in automated livestock management applications. This approach reduces errors in automated milking systems and improves overall efficiency in livestock monitoring.
6. The system of claim 1 , wherein determining whether the teat candidate satisfies one or more filtering rules comprises: determining a percentage of B-points that are located below the A-points in the 3D image; comparing the determined percentage to a B-point ratio threshold value; and determining the teat candidate satisfies a filtering rule in response to determining that the determined percentage is less than or equal to the B-point ratio threshold value.
This invention relates to automated systems for identifying and analyzing teat candidates in 3D images, particularly in agricultural or veterinary applications where precise teat detection is critical for tasks like milking or health monitoring. The system addresses challenges in accurately distinguishing teats from other anatomical features or background noise in 3D imaging data, which can lead to errors in automated processes. The system processes a 3D image to identify teat candidates by analyzing spatial relationships between points in the image. Specifically, it evaluates the positions of A-points and B-points, where A-points represent higher elevation features and B-points represent lower elevation features. The system calculates the percentage of B-points located below the A-points within a teat candidate region and compares this percentage to a predefined B-point ratio threshold. If the percentage is below or equal to the threshold, the teat candidate is deemed valid, filtering out false positives that do not meet the expected anatomical structure of a teat. This method improves the accuracy of teat detection by leveraging spatial point distribution patterns, ensuring only valid teat candidates are selected for further processing. The approach is particularly useful in automated milking systems or livestock monitoring, where reliable teat identification is essential for operational efficiency and animal welfare.
7. The system of claim 1 , further comprising consolidating the filtered set of teat candidates, wherein consolidating the filtered set of teat candidates comprises: identifying a subset of teat candidates having teat locations within a first proximity threshold; and combining the set of A-points and the set of B-points for the teat candidates in the subset of teat candidates.
This invention relates to automated systems for identifying and processing teat candidates in livestock, particularly for milking applications. The problem addressed is the accurate detection and consolidation of teat locations to improve milking robot performance. The system first filters potential teat candidates based on spatial and geometric criteria, such as distance and angle thresholds, to eliminate false positives. It then consolidates the remaining teat candidates by grouping those within a defined proximity threshold. For each group, the system combines their respective A-points (likely representing teat tips) and B-points (likely representing teat bases) to refine the teat location data. This consolidation step ensures that multiple detections of the same teat are merged into a single, more accurate representation. The system may also include additional steps such as generating a bounding box around the consolidated teat candidates to further refine their spatial representation. The overall goal is to enhance the precision of teat detection, reducing errors in automated milking systems.
8. The system of claim 7 , wherein combining the set of A-points and the set of B-points for the teat candidates in the subset of teat candidates comprises averaging the set of A-points and the set of B-points for the teat candidates in the subset of teat candidates.
This invention relates to a system for processing images of livestock teats to identify and analyze teat candidates. The system addresses the challenge of accurately detecting and measuring teats in images, which is critical for tasks such as milking management and animal health monitoring. The system processes images to generate a subset of teat candidates, each associated with a set of A-points and B-points representing key features of the teats. To refine the detection, the system combines the A-points and B-points for the teat candidates in the subset by averaging them. This averaging step improves the accuracy of teat localization by reducing noise and variability in the detected points. The system may also include additional steps such as filtering the subset of teat candidates based on predefined criteria, such as size or shape, to further enhance detection reliability. The invention is particularly useful in automated dairy farming systems where precise teat identification is essential for efficient milking operations.
9. A teat identification method, comprising: positioning, by a processor, a robotic arm adjacent to a dairy livestock, wherein the robotic arm comprises a three-dimensional (3D) camera disposed on the robotic arm; acquiring, by the 3D camera, a 3D image, wherein each pixel of the 3D image is associated with a depth value; identifying, by the processor, a set of teat candidates within the 3D image, wherein each teat candidate is associated with a teat location and a set of pixels in the 3D image; filtering, by the processor, the set of teat candidates, wherein filtering the set of teat candidates comprises: identifying a set of A-points for a teat candidate, wherein A-points are pixels within a first distance threshold of the teat location for the teat candidate; identifying a set of B-points for the teat candidate, wherein B-points are pixels within a second distance threshold of the teat location for the teat candidate, wherein the second distance threshold is greater than the first distance threshold; determining whether the teat candidate satisfies one or more filtering rules based on the A-points and the B-points; and removing the teat candidate from the set of teat candidates in response to determining that the teat candidate fans to satisfy at least one of the filtering rules; determining, by the processor, an aggregate teat candidate score for the set of teat candidates, wherein the aggregate teat candidate score is based at least in part on distances between teat candidates and the teat locations for the set of teat candidates of the dairy livestock; comparing, by the processor, the aggregate teat candidate score to a score threshold value; and updating, by the processor, the teat location information for the dairy livestock in response to determining that the aggregate teat candidate score is greater than or equal to the score threshold value.
The invention relates to automated teat identification in dairy livestock using a robotic arm equipped with a 3D camera. The system addresses the challenge of accurately detecting and locating teats for automated milking processes, which is critical for efficient and hygienic dairy operations. The method involves positioning the robotic arm near the livestock and capturing a 3D image where each pixel has an associated depth value. The processor then identifies potential teat candidates within the image, each defined by a location and a set of pixels. To refine the candidates, the system filters them by analyzing two sets of pixels: A-points (within a first distance threshold of the teat location) and B-points (within a larger second distance threshold). Filtering rules are applied to these points to eliminate false positives. The remaining candidates are scored based on their spatial relationships, and if the aggregate score meets a predefined threshold, the teat location data is updated. This approach ensures reliable teat detection for automated milking systems.
10. The method of claim 9 , wherein determining whether the teat candidate satisfies one or more filtering rules comprises: determining a total number of A-points and B-points for the teat candidate; comparing the total number of A-points and B-points to a total point count value; and determining the teat candidate satisfies a filtering rule in response to determining that the total number of A-points and B-points is greater than or equal to the total point count value.
The invention relates to automated teat detection in livestock, particularly for identifying teat candidates in images of animals such as cows. The problem addressed is accurately distinguishing true teats from false positives in automated detection systems, which is critical for tasks like milking or health monitoring. The method involves analyzing teat candidates by evaluating specific feature points, referred to as A-points and B-points, which are key indicators of teat presence. A-points and B-points are likely derived from image processing techniques that identify distinct visual characteristics of teats, such as shape, contrast, or texture. The method determines whether a teat candidate meets predefined criteria by counting these points and comparing the total to a threshold value. If the combined count of A-points and B-points exceeds or meets this threshold, the candidate is classified as a valid teat. This filtering step ensures that only reliable teat detections are retained, improving the accuracy of automated systems. The approach may be part of a broader system that includes image capture, preprocessing, and initial candidate identification, but focuses specifically on the validation phase using point-based analysis.
11. The method of claim 9 , wherein determining whether the teat candidate satisfies one or more filtering rules comprises: determining a total number of A-points for the teat candidate; comparing the total number of A-points with an A-point count threshold value; and determining the teat candidate satisfies a filtering rule in response to determining that the total number of A-points is greater than or equal to the A-point count threshold value.
This invention relates to automated systems for identifying and analyzing teat candidates in livestock, particularly for tasks like milking or health monitoring. The problem addressed is accurately distinguishing true teats from false positives in images or sensor data, which is critical for reliable automated dairy farming operations. The method involves processing image or sensor data to detect potential teat candidates. For each candidate, the system determines a total number of A-points, which are specific features or markers associated with the teat. These A-points may include geometric characteristics, texture patterns, or other distinguishing attributes extracted from the data. The system then compares the total number of A-points against a predefined threshold value. If the count meets or exceeds this threshold, the teat candidate is deemed valid and satisfies the filtering rule, indicating it is likely a true teat. This filtering step helps reduce false positives, improving the accuracy of teat detection in automated systems. The threshold value can be adjusted based on environmental conditions, lighting, or other factors to optimize performance. This approach enhances the reliability of automated milking or health monitoring systems in dairy farming.
12. The method of claim 9 , wherein determining whether the teat candidate satisfies one or more filtering rules comprises: determining a width of the teat candidate based at least in part on the A-points; comparing the width of the teat candidate to a width range limit; and determining the teat candidate satisfies a filtering rule in response to determining that the width of the teat candidate is within the width range limit.
This invention relates to automated systems for identifying and analyzing teat candidates in livestock, particularly for tasks such as milking or health monitoring. The problem addressed is the accurate and efficient detection of teats in images or sensor data, which is challenging due to variations in size, shape, and environmental conditions. The method involves processing image or sensor data to identify potential teat candidates, which are regions of interest that may correspond to an animal's teat. To refine these candidates, the method applies filtering rules based on physical characteristics. Specifically, the width of each teat candidate is calculated using predefined reference points (A-points). The calculated width is then compared to a predefined width range limit. If the width falls within this range, the teat candidate is deemed valid and retained for further processing; otherwise, it is discarded. This filtering step ensures that only plausible teat candidates are considered, improving the accuracy of subsequent analysis. The method may be part of a larger system for automated milking, health monitoring, or other livestock management applications. The use of width-based filtering helps distinguish true teats from false positives, such as background noise or other body parts. The technique is particularly useful in automated systems where manual intervention is impractical or inefficient.
13. The method of claim 9 , wherein determining whether the teat candidate satisfies one or more filtering rules comprises: determining a height of the teat candidate based at least in part on the A-points; comparing the height of the teat candidate to a height range limit; and determining the teat candidate satisfies a filtering rule in response to determining that the height of the teat candidate is within the height range limit.
This invention relates to automated systems for identifying and filtering teat candidates in livestock, particularly for robotic milking applications. The problem addressed is accurately detecting and validating teat positions to ensure proper attachment of milking equipment, improving efficiency and reducing errors in automated milking systems. The method involves analyzing teat candidates detected in an image or sensor data, where each teat candidate is represented by a set of anatomical reference points, including A-points that define key locations on the teat. The system determines the height of each teat candidate by evaluating the spatial relationship of these A-points. The height is then compared against predefined height range limits to filter out invalid candidates. Only teat candidates with heights within the acceptable range are considered valid, ensuring that only properly positioned teats are selected for milking. This filtering step helps eliminate false positives, such as misidentified body parts or environmental noise, improving the reliability of automated milking systems. The height-based filtering is part of a broader validation process that may include additional checks, such as shape or position analysis, to further refine teat detection accuracy. The method ensures that only correctly identified teats proceed to the next stage, reducing attachment failures and enhancing overall system performance.
14. The method of claim 9 , wherein determining whether the teat candidate satisfies one or more filtering rules comprises: determining a percentage of B-points that are located below the A-points in the 3D image; comparing the determined percentage to a B-point ratio threshold value; and determining the teat candidate satisfies a filtering rule in response to determining that the determined percentage is less than or equal to the B-point ratio threshold value.
This invention relates to automated teat detection in 3D images, particularly for livestock monitoring systems. The problem addressed is accurately identifying teat locations in 3D images while minimizing false positives, which is challenging due to variations in animal anatomy and imaging conditions. The method involves analyzing a 3D image to detect teat candidates by identifying clusters of points (A-points) that may represent teats. To refine these candidates, the method evaluates the spatial relationship between these A-points and other points (B-points) in the image. Specifically, it calculates the percentage of B-points located below the A-points in the 3D space. This percentage is then compared to a predefined threshold value. If the percentage is below or equal to the threshold, the teat candidate is deemed valid. This filtering step helps eliminate false detections by ensuring that the detected teat-like structures have the expected spatial characteristics relative to surrounding points. The method improves accuracy in automated livestock monitoring by reducing incorrect teat identifications.
15. The method of claim 9 , further comprising consolidating the filtered set of teat candidates, wherein consolidating the filtered set of teat candidates comprises: identifying a subset of teat candidates having teat locations within a first proximity threshold; and combining the set of A-points and the set of B-points for the teat candidates in the subset of teat candidates.
This invention relates to automated systems for identifying and processing teat candidates in livestock, particularly for tasks like milking or health monitoring. The problem addressed is accurately detecting and consolidating teat locations from multiple candidate points to improve precision in automated livestock management systems. The method involves analyzing image data to detect potential teat locations, represented as sets of A-points and B-points. A-points and B-points are spatial coordinates or features derived from image processing that correspond to different aspects of teat detection, such as position and orientation. The method filters these candidates based on predefined criteria to reduce false positives. After filtering, the method further refines the results by consolidating nearby teat candidates. This consolidation step involves identifying subsets of teat candidates where the teat locations are within a first proximity threshold, indicating they likely represent the same teat. The method then combines the A-points and B-points from these candidates to produce a more accurate and unified representation of the teat's position and characteristics. This ensures that overlapping or closely spaced detections are merged, improving the reliability of downstream applications like robotic milking or health assessments. The approach enhances accuracy by reducing redundancy and refining spatial data.
16. The method of claim 15 , wherein combining the set of A-points and the set of B-points for the teat candidates in the subset of teat candidates comprises averaging the set of A-points and the set of B-points for the teat candidates in the subset of teat candidates.
This invention relates to image processing techniques for identifying and analyzing teat candidates in images, particularly in agricultural or veterinary applications such as automated milking systems. The problem addressed is accurately detecting and localizing teat positions in images to facilitate automated tasks like robotic milking, where precise teat identification is critical for proper attachment of milking equipment. The method involves processing an image to detect teat candidates, which are potential teat locations identified through initial image analysis. These candidates are refined by combining sets of A-points and B-points associated with each teat candidate. A-points and B-points are specific feature points or coordinates derived from the image, representing distinct characteristics of the teat candidates. The refinement process involves averaging the A-points and B-points for a subset of the teat candidates to improve the accuracy of teat localization. This averaging step helps reduce noise and variability in the detected teat positions, ensuring more reliable identification for subsequent automated operations. The technique leverages geometric or statistical averaging to enhance the precision of teat detection, which is essential for systems requiring high accuracy in teat positioning.
17. An apparatus, comprising; a memory operable to store teat location information for a dairy livestock; and a processor operably coupled to the memory, and configured to: position a robotic arm adjacent to the dairy livestock; communicate an electrical signal to trigger acquiring a 3D image using a 3D camera, wherein each pixel of the 3D image is associated with a depth value; identify a set of teat candidates within the 3D image, wherein each teat candidate is associated with a teat location and a set of pixels in the 3D image; filter the set of teat candidates, wherein filtering the set of teat candidates comprises: identifying a set of A-points for a teat candidate, wherein A-points are pixels within a first distance threshold of the teat location for the teat candidate; identifying a set of B-points for the teat candidate, wherein B-points are pixels within a second distance threshold of the teat location for the teat candidate, wherein the second distance threshold is greater than the first distance threshold; determining whether the teat candidate satisfies one or more filtering rules based on the A-points and the B-points; and removing the teat candidate from the set of teat candidates in response to determining that the teat candidate fans to satisfy at least one of the filtering rules; determine an aggregate teat candidate score for the set of teat candidates, wherein the aggregate teat candidate score is based at least in part on distances between teat candidates and the teat locations for the set of test candidates of the dairy livestock; compare the aggregate teat candidate score to a score threshold value; and update the teat location information for the dairy livestock in response to determining that the aggregate teat candidate score is greater than or equal to the score threshold value.
The invention relates to automated milking systems for dairy livestock, specifically a method for accurately identifying and locating teats using 3D imaging and filtering techniques. The system addresses challenges in robotic milking, where precise teat detection is critical for efficient and hygienic milking operations. The apparatus includes a memory storing teat location data and a processor that controls a robotic arm and a 3D camera. The processor triggers the camera to capture a 3D image, where each pixel has a depth value, and identifies potential teat candidates based on their locations and associated pixels. To refine the candidates, the system filters them by analyzing two sets of pixels: A-points (within a close proximity to the teat location) and B-points (within a larger proximity). Filtering rules are applied to these points to eliminate false positives. The remaining candidates are scored based on their spatial relationships, and if the aggregate score meets a predefined threshold, the teat location data is updated. This ensures reliable teat detection for automated milking processes.
18. The apparatus of claim 17 , wherein determining whether the teat candidate satisfies one or more filtering rules comprises: determining a total number of A-points for the teat candidate; comparing the total number of A-points with an A-point count threshold value; and determining the teat candidate satisfies a filtering rule in response to determining that the total number of A-points is greater than or equal to the A-point count threshold value.
This invention relates to automated systems for identifying and analyzing teat candidates in livestock, particularly for tasks like milking or health monitoring. The problem addressed is accurately detecting and validating teat candidates in images or sensor data to ensure proper equipment positioning and animal welfare. The apparatus includes a processing system that evaluates teat candidates by analyzing A-points, which are specific features or markers associated with teats. The system determines whether a teat candidate meets predefined filtering rules by counting the total number of A-points for the candidate and comparing this count to a threshold value. If the count meets or exceeds the threshold, the teat candidate is deemed valid. This filtering step helps eliminate false positives, ensuring only genuine teats are selected for further processing or interaction. The apparatus may also include imaging or sensor components to capture data of the animal's udder, and algorithms to detect initial teat candidates based on shape, texture, or other characteristics. The A-point analysis refines these candidates, improving accuracy in automated milking or health assessment systems. The threshold value can be adjusted based on environmental conditions, animal breed, or system requirements to optimize performance. This method enhances reliability in automated livestock management by reducing errors in teat detection.
19. The apparatus of claim 17 , wherein determining whether the teat candidate satisfies one or more filtering rules comprises: determining a width of the teat candidate based at least in part on the A-points; comparing the width of the teat candidate to a width range limit; and determining the teat candidate satisfies a filtering rule in response to determining that the width of the teat candidate is within the width range limit.
This invention relates to automated systems for identifying and analyzing teat candidates in livestock, particularly for milking or health monitoring applications. The problem addressed is the accurate detection of teats in images or sensor data to ensure proper attachment of milking equipment or to assess animal health. The apparatus includes a sensor system to capture data of an animal's udder region and a processing unit that identifies potential teat candidates based on detected features, such as A-points (key anatomical landmarks). The system then applies filtering rules to validate these candidates. One such rule involves measuring the width of a teat candidate, derived from the A-points, and comparing it to a predefined width range. If the measured width falls within this range, the candidate is deemed valid. This filtering step helps eliminate false positives, improving the reliability of teat detection. The apparatus may further include additional processing steps, such as refining the position of the teat candidate or adjusting the sensor system based on the detection results. The overall goal is to enhance the precision and efficiency of automated livestock management systems.
20. The apparatus of claim 17 , wherein determining whether the teat candidate satisfies one or more filtering rules comprises: determining a percentage of B-points that are located below the A-points in the 3D image; comparing the determined percentage to a B-point ratio threshold value; and determining the teat candidate satisfies a filtering rule in response to determining that the determined percentage is less than or equal to the B-point ratio threshold value.
This invention relates to automated systems for identifying and analyzing teat candidates in 3D images, particularly in agricultural or veterinary applications where precise detection of animal teats is required for tasks such as milking or health monitoring. The problem addressed is the accurate identification of teats in 3D images, where false positives or missed detections can lead to inefficiencies or errors in automated processes. The apparatus includes a 3D imaging system that captures a 3D image of an animal, such as a cow, and processes the image to detect potential teat candidates. The detection process involves identifying A-points and B-points in the 3D image, where A-points represent higher elevation points and B-points represent lower elevation points relative to a reference plane. The apparatus then evaluates whether a teat candidate satisfies one or more filtering rules by calculating the percentage of B-points located below the A-points in the 3D image. This percentage is compared to a predefined B-point ratio threshold value. If the determined percentage is less than or equal to the threshold, the teat candidate is deemed valid. This filtering step helps eliminate false positives by ensuring that the detected teat candidate has a sufficient number of B-points below the A-points, which is characteristic of a true teat structure. The system may also include additional processing steps, such as refining the detected teat candidates or integrating the results with other automated systems for further actions.
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February 11, 2020
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